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ephen-experiments

event network completion experiments with ephen and other information network embedding methods experiments run from graphs at graph_embeddings.py distilbert can be applied on gdelt data with text in at distilbert_from_gdelt.py graphs can be generated from distibert and gdelt data at ephen_utils.py

how to cite

@INPROCEEDINGS{9671645,  
  author={do Carmo, Paulo and Marcacini, Ricardo},  
  booktitle={2021 IEEE International Conference on Big Data (Big Data)},   
  title={Embedding propagation over heterogeneous event networks for link prediction},   
  year={2021},  
  volume={}, 
  number={},  
  pages={4812-4821},  
  doi={10.1109/BigData52589.2021.9671645}
}

GraphEmbeddings

GraphEmbeddings submodule based on https://github.com/shenweichen/GraphEmbedding but the used algorithms works with tf 2.x

install

inside GraphEmbeddings directory from this repository run

python setup.py install

gcn

GCN submodule based on https://github.com/dbusbridge/gcn_tutorial

networks

all the events represented in the context sub-netwokrs were extracted from the GDELT project (https://www.gdeltproject.org/) and are represented as networkx (https://networkx.org) pickle5 graphs. all the networks have DistilBERT-multilingual (https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased) embedding for the events, and other objects available from the GDELT project database. you can download them at: https://drive.google.com/drive/folders/1_Wz9O4Nzr8JgjzbMzMHI54M3LITG7PCZ?usp=sharing

networks like these are required to the experiments, so download them (or create yours) to an accessible directory on your machine and update the path on the code.

other results

a compilation of other results from experiments with their respective targets MAP averages and standard deviation can be found at: https://docs.google.com/spreadsheets/d/1ub2Fya25PS0GDY0joM8HOe3vCk7SY7xMgXaaMzPdK6Q/edit?usp=sharing